Recap last lecture

  • from unique words to contextual embeddings 🧮
    • more granular representations are more effective
  • modern, data-driven NLP is both powerful and biased 🚨
    • there is nothing like raw data
    • reflect the representation and decisions behind it

Outline

  • get some organizational stuff done
  • let’s do serious NLP! ✨
  • code interactively
    • interrupt, ask, and complement

Organizational

Tell me… 📣


Thanks for any constructive feedback,
be it sweet or sour! 🙏

Your mini-projects

  • Your project idea is recorded here
  • You are ready to work on it (self-paced)
  • Reach out if you are stuck! 🤯

What is a word?

  • words ~ segments between whitespace
  • yet, there are …
    • contractions: U.S., don't
    • collocations: New York

Token

  • token ~ computational unit
    • representation of words
  • lemma ~ base form of a word
    • texts → text
    • goes → go
  • stop words ~ functional words
    • lacking deeper meaning
    • the, a, on, and …

Segmenting a text into tokens


Let's tokenize this sentence! Isn't is easy? 🤓

Classic processing steps in NLP

  1. Tokenizing
    • segmenting text into words, punctuation etc.
  2. Tagging part-of-speech (POS)
    • assigning word types (e.g. verb, noun)
  3. Parsing
    • describing syntactic relations
  4. Named Entity Recognition (NER)
    • organizations, persons, locations, time etc.

Automatically inferred information of a sentence


👉 Catch up on NLP with
Jurafsky and Martin (forthcoming)

Let’s apply this in practice ✨

References

Jurafsky, Dan, and James H. Martin. forthcoming. Speech and Language Processing. 3rd (Feb 3, 2024 draft). London: Prentice Hall. https://web.stanford.edu/~jurafsky/slp3/.